




'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 	_snake_case(			UpperCAmelCase       , UpperCAmelCase       , UpperCAmelCase       , unittest.TestCase      ):
 __snake_case:							Optional[int]						=					StableDiffusionInstructPixaPixPipeline
 __snake_case:							Optional[int]						=					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
 __snake_case:							Optional[Any]						=					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 __snake_case:							Union[str, Any]						=					IMAGE_TO_IMAGE_IMAGE_PARAMS
 __snake_case:							List[Any]						=					IMAGE_TO_IMAGE_IMAGE_PARAMS






 def 						_UpperCamelCase (self       :  str    )       ->   Any:






    """simple docstring"""


    torch.manual_seed(0    )
    A__       =     UNetaDConditionModel(
        block_out_channels=(32, 64)    ,		layers_per_block=2    ,		sample_size=32    ,		in_channels=8    ,		out_channels=4    ,		down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')    ,		up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')    ,		cross_attention_dim=32    ,		)
    A__       =     PNDMScheduler(skip_prk_steps=a    )
    torch.manual_seed(0    )
    A__       =     AutoencoderKL(
        block_out_channels=[32, 64]    ,		in_channels=3    ,		out_channels=3    ,		down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']    ,		up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']    ,		latent_channels=4    ,		)
    torch.manual_seed(0    )
    A__       =     CLIPTextConfig(
        bos_token_id=0    ,		eos_token_id=2    ,		hidden_size=32    ,		intermediate_size=37    ,		layer_norm_eps=1e-05    ,		num_attention_heads=4    ,		num_hidden_layers=5    ,		pad_token_id=1    ,		vocab_size=10_00    ,		)
    A__       =     CLIPTextModel(a    )
    A__       =     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

    A__       =     {
        'unet': unet,
        'scheduler': scheduler,
        'vae': vae,
        'text_encoder': text_encoder,
        'tokenizer': tokenizer,
        'safety_checker': None,
        'feature_extractor': None,
    }
    return components






 def 						_UpperCamelCase (self       :  Optional[int]    ,		a       :  Any    ,		a       :  Dict=0    )       ->   str:






    """simple docstring"""


    A__       =     floats_tensor((1, 3, 32, 32)    ,		rng=random.Random(a    )    ).to(a    )
    A__       =     image.cpu().permute(0    ,		2    ,		3    ,		1    )[0]
    A__       =     Image.fromarray(np.uinta(a    )    ).convert('RGB'    )
    if str(a    ).startswith('mps'    ):
       A__       =     torch.manual_seed(a    )
    else:
       A__       =     torch.Generator(device=a    ).manual_seed(a    )
    A__       =     {
        'prompt': 'A painting of a squirrel eating a burger',
        'image': image,
        'generator': generator,
        'num_inference_steps': 2,
        'guidance_scale': 6.0,
        'image_guidance_scale': 1,
        'output_type': 'numpy',
    }
    return inputs






 def 						_UpperCamelCase (self       :  Dict    )       ->   int:






    """simple docstring"""


    A__       =     'cpu'  # ensure determinism for the device-dependent torch.Generator
    A__       =     self.get_dummy_components()
    A__       =     StableDiffusionInstructPixaPixPipeline(**a    )
    A__       =     sd_pipe.to(a    )
    sd_pipe.set_progress_bar_config(disable=a    )

    A__       =     self.get_dummy_inputs(a    )
    A__       =     sd_pipe(**a    ).images
    A__       =     image[0, -3:, -3:, -1]
    assert image.shape == (1, 32, 32, 3)
    A__       =     np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]    )

    assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-3






 def 						_UpperCamelCase (self       :  int    )       ->   Union[str, Any]:






    """simple docstring"""


    A__       =     'cpu'  # ensure determinism for the device-dependent torch.Generator
    A__       =     self.get_dummy_components()
    A__       =     StableDiffusionInstructPixaPixPipeline(**a    )
    A__       =     sd_pipe.to(a    )
    sd_pipe.set_progress_bar_config(disable=a    )

    A__       =     self.get_dummy_inputs(a    )
    A__       =     'french fries'
    A__       =     sd_pipe(**a    ,		negative_prompt=a    )
    A__       =     output.images
    A__       =     image[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    A__       =     np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]    )

    assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-3






 def 						_UpperCamelCase (self       :  Optional[int]    )       ->   Optional[Any]:






    """simple docstring"""


    A__       =     'cpu'  # ensure determinism for the device-dependent torch.Generator
    A__       =     self.get_dummy_components()
    A__       =     StableDiffusionInstructPixaPixPipeline(**a    )
    A__       =     sd_pipe.to(a    )
    sd_pipe.set_progress_bar_config(disable=a    )

    A__       =     self.get_dummy_inputs(a    )
    A__       =     [inputs['prompt']] * 2

    A__       =     np.array(inputs['image']    ).astype(np.floataa    ) / 255.0
    A__       =     torch.from_numpy(a    ).unsqueeze(0    ).to(a    )
    A__       =     image / 2 + 0.5
    A__       =     image.permute(0    ,		3    ,		1    ,		2    )
    A__       =     image.repeat(2    ,		1    ,		1    ,		1    )

    A__       =     sd_pipe(**a    ).images
    A__       =     image[-1, -3:, -3:, -1]

    assert image.shape == (2, 32, 32, 3)
    A__       =     np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]    )

    assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-3






 def 						_UpperCamelCase (self       :  Union[str, Any]    )       ->   Union[str, Any]:






    """simple docstring"""


    A__       =     'cpu'  # ensure determinism for the device-dependent torch.Generator
    A__       =     self.get_dummy_components()
    A__       =     EulerAncestralDiscreteScheduler(
        beta_start=0.0_0085    ,		beta_end=0.012    ,		beta_schedule='scaled_linear'    )
    A__       =     StableDiffusionInstructPixaPixPipeline(**a    )
    A__       =     sd_pipe.to(a    )
    sd_pipe.set_progress_bar_config(disable=a    )

    A__       =     self.get_dummy_inputs(a    )
    A__       =     sd_pipe(**a    ).images
    A__       =     image[0, -3:, -3:, -1]

    A__       =     [round(a    ,		4    ) for x in image_slice.flatten().tolist()]
    print(','.join([str(a    ) for x in slice]    )    )

    assert image.shape == (1, 32, 32, 3)
    A__       =     np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]    )

    assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-3






 def 						_UpperCamelCase (self       :  List[Any]    )       ->   Any:






    """simple docstring"""


    super().test_inference_batch_single_identical(expected_max_diff=3e-3    )






 def 						_UpperCamelCase (self       :  Any    )       ->   str:






    """simple docstring"""


    A__       =     self.get_dummy_components()
    A__       =     StableDiffusionInstructPixaPixPipeline(**a    )
    A__       =     VaeImageProcessor(do_resize=a    ,		do_normalize=a    )
    A__       =     pipe.to(a    )
    pipe.set_progress_bar_config(disable=a    )

    A__       =     pipe(**self.get_dummy_inputs_by_type(a    ,		input_image_type='pt'    )    )[0]

    A__       =     components['vae']
    A__       =     self.get_dummy_inputs_by_type(a    ,		input_image_type='pt'    )

    for image_param in self.image_latents_params:
       if image_param in inputs.keys():
          A__       =     vae.encode(inputs[image_param]    ).latent_dist.mode()

    A__       =     pipe(**a    )[0]

    A__       =     np.abs(out - out_latents_inputs    ).max()
    self.assertLess(a    ,		1e-4    ,		'passing latents as image input generate different result from passing image'    )




@slow
@require_torch_gpu
class 	_snake_case(			unittest.TestCase      ):






 def 						_UpperCamelCase (self       :  List[str]    )       ->   Union[str, Any]:






    """simple docstring"""


    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()






 def 						_UpperCamelCase (self       :  Dict    ,		a       :  Union[str, Any]=0    )       ->   Dict:






    """simple docstring"""


    A__       =     torch.manual_seed(a    )
    A__       =     load_image(
        'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'    )
    A__       =     {
        'prompt': 'turn him into a cyborg',
        'image': image,
        'generator': generator,
        'num_inference_steps': 3,
        'guidance_scale': 7.5,
        'image_guidance_scale': 1.0,
        'output_type': 'numpy',
    }
    return inputs






 def 						_UpperCamelCase (self       :  List[str]    )       ->   Optional[Any]:






    """simple docstring"""


    A__       =     StableDiffusionInstructPixaPixPipeline.from_pretrained(
        'timbrooks/instruct-pix2pix'    ,		safety_checker=a    )
    pipe.to(a    )
    pipe.set_progress_bar_config(disable=a    )
    pipe.enable_attention_slicing()

    A__       =     self.get_inputs()
    A__       =     pipe(**a    ).images
    A__       =     image[0, -3:, -3:, -1].flatten()

    assert image.shape == (1, 5_12, 5_12, 3)
    A__       =     np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]    )

    assert np.abs(expected_slice - image_slice    ).max() < 1e-3






 def 						_UpperCamelCase (self       :  List[str]    )       ->   str:






    """simple docstring"""


    A__       =     StableDiffusionInstructPixaPixPipeline.from_pretrained(
        'timbrooks/instruct-pix2pix'    ,		safety_checker=a    )
    A__       =     LMSDiscreteScheduler.from_config(pipe.scheduler.config    )
    pipe.to(a    )
    pipe.set_progress_bar_config(disable=a    )
    pipe.enable_attention_slicing()

    A__       =     self.get_inputs()
    A__       =     pipe(**a    ).images
    A__       =     image[0, -3:, -3:, -1].flatten()

    assert image.shape == (1, 5_12, 5_12, 3)
    A__       =     np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]    )

    assert np.abs(expected_slice - image_slice    ).max() < 1e-3






 def 						_UpperCamelCase (self       :  Tuple    )       ->   Dict:






    """simple docstring"""


    A__       =     StableDiffusionInstructPixaPixPipeline.from_pretrained(
        'timbrooks/instruct-pix2pix'    ,		safety_checker=a    )
    A__       =     DDIMScheduler.from_config(pipe.scheduler.config    )
    pipe.to(a    )
    pipe.set_progress_bar_config(disable=a    )
    pipe.enable_attention_slicing()

    A__       =     self.get_inputs()
    A__       =     pipe(**a    ).images
    A__       =     image[0, -3:, -3:, -1].flatten()

    assert image.shape == (1, 5_12, 5_12, 3)
    A__       =     np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]    )

    assert np.abs(expected_slice - image_slice    ).max() < 1e-3






 def 						_UpperCamelCase (self       :  List[Any]    )       ->   int:






    """simple docstring"""


    A__       =     0

    def callback_fn(a       :  int    ,		a       :  int    ,		a       :  torch.FloatTensor    ) -> None:
       A__       =     True
       nonlocal number_of_steps
       number_of_steps += 1
       if step == 1:
          A__       =     latents.detach().cpu().numpy()
          assert latents.shape == (1, 4, 64, 64)
          A__       =     latents[0, -3:, -3:, -1]
          A__       =     np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]    )

          assert np.abs(latents_slice.flatten() - expected_slice    ).max() < 5e-2
       elif step == 2:
          A__       =     latents.detach().cpu().numpy()
          assert latents.shape == (1, 4, 64, 64)
          A__       =     latents[0, -3:, -3:, -1]
          A__       =     np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]    )

          assert np.abs(latents_slice.flatten() - expected_slice    ).max() < 5e-2

    A__       =     False

    A__       =     StableDiffusionInstructPixaPixPipeline.from_pretrained(
        'timbrooks/instruct-pix2pix'    ,		safety_checker=a    ,		torch_dtype=torch.floataa    )
    A__       =     pipe.to(a    )
    pipe.set_progress_bar_config(disable=a    )
    pipe.enable_attention_slicing()

    A__       =     self.get_inputs()
    pipe(**a    ,		callback=a    ,		callback_steps=1    )
    assert callback_fn.has_been_called
    assert number_of_steps == 3






 def 						_UpperCamelCase (self       :  List[Any]    )       ->   List[str]:






    """simple docstring"""


    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()
    torch.cuda.reset_peak_memory_stats()

    A__       =     StableDiffusionInstructPixaPixPipeline.from_pretrained(
        'timbrooks/instruct-pix2pix'    ,		safety_checker=a    ,		torch_dtype=torch.floataa    )
    A__       =     pipe.to(a    )
    pipe.set_progress_bar_config(disable=a    )
    pipe.enable_attention_slicing(1    )
    pipe.enable_sequential_cpu_offload()

    A__       =     self.get_inputs()
    A__       =     pipe(**a    )

    A__       =     torch.cuda.max_memory_allocated()
    # make sure that less than 2.2 GB is allocated
    assert mem_bytes < 2.2 * 10**9






 def 						_UpperCamelCase (self       :  int    )       ->   str:






    """simple docstring"""


    A__       =     self.get_inputs()
    # resize to resolution that is divisible by 8 but not 16 or 32
    A__       =     inputs['image'].resize((5_04, 5_04)    )

    A__       =     'timbrooks/instruct-pix2pix'
    A__       =     StableDiffusionInstructPixaPixPipeline.from_pretrained(
        a    ,		safety_checker=a    ,		)
    pipe.to(a    )
    pipe.set_progress_bar_config(disable=a    )
    pipe.enable_attention_slicing()

    A__       =     pipe(**a    )
    A__       =     output.images[0]

    A__       =     image[2_55:2_58, 3_83:3_86, -1]

    assert image.shape == (5_04, 5_04, 3)
    A__       =     np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]    )

    assert np.abs(image_slice.flatten() - expected_slice    ).max() < 5e-3








'''simple docstring'''







from torch import nn







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   if act_fn in ["swish", "silu"]:
      return nn.SiLU()
   elif act_fn == "mish":
      return nn.Mish()
   elif act_fn == "gelu":
      return nn.GELU()
   else:
      raise ValueError(F"""Unsupported activation function: {act_fn}"""						)


